Inferring Asteroseismic Parameters from Short Observations Using Deep Learning: Application to TESS and K2 Red Giants

2026-05-08

AI Summary

Traditional methods struggle to efficiently and accurately extract key asteroseismic parameters—such as the large frequency separation (Δν), the frequency of maximum power (ν_max), and the period spacing of dipole modes (ΔΠ₁)—from the short-duration (1–2 month) red giant light curves provided by TESS and K2, due to limitations in computational cost and data quality. This work proposes the first end-to-end deep learning framework capable of simultaneously inferring these three parameters directly from short-baseline photometric time series, thereby circumventing the reliance of conventional spectral fitting on long data spans and high signal-to-noise ratios. Trained on a combination of synthetic and real data, the model accurately recovers Δν and ν_max for approximately 50% of K2 targets, yields reliable Δν estimates for 23% of single-sector TESS observations, and successfully identifies ΔΠ₁ for around 200 young red giants, consistent with the established Δν–ΔΠ₁ degeneracy relation, significantly enhancing the efficiency of asteroseismic analysis in large-scale surveys.

Abstract

Asteroseismology is the study of resonant oscillations of stars to infer their internal structure and dynamics. It is also a powerful tool for precisely determining stellar parameters such as mass, radius, surface gravity, and age. The ongoing TESS mission, with its nearly complete sky coverage, presents a unique opportunity to uniformly probe stellar populations across the Milky Way. TESS is estimated to have observed more than 300,000 oscillating red giants, most of which have one to two months of observations. Given the scale of this dataset, we need a fast, efficient, and robust way to analyse the data. In this work, our objective is to develop a machine learning (ML) based method to infer asteroseismic parameters from short-duration observations. Specifically, we focus on two global seismic parameters, the large frequency separation ($Δν$) and the frequency at maximum power ($ν_{\mathrm{max}}$), from one-month-long TESS observations of red giants. Meanwhile, for K2 data, our focus extends to inferring the period spacings of dipolar gravity modes ($ΔΠ_{1}$), in addition to $Δν$ and $ν_{\mathrm{max}}$. Our findings demonstrate that our machine learning algorithm can accurately infer $Δν$ and $ν_{\mathrm{max}}$ for approximately 50% of samples created by taking one-month Kepler and K2 observations. For TESS one sector data however, we recover reliable $Δν$ for only about 23% of the stars. Additionally, we get reliable $ΔΠ_{1}$ inferences for about 200 young red-giants from K2. For these $ΔΠ_{1}$ inferences, we see a good match with the well known $Δν-ΔΠ_{1}$ degenerate sequence observed in Kepler red-giants.

Problem

• asteroseismology
• red giants
• TESS
• K2
• seismic parameters

Innovation

• deep learning
• asteroseismology
• red giants
• TESS
• K2